Denoising diffusion probabilistic models (DDPM) [1]}, [2]}
are a class of generative models that can learn to convert unstructured noise to samples from a given distribution, by performing an iterative process of removing small amounts of Gaussian noise at each step.
Since our method heavily relies on DDPM, we provide here a very brief overview of DDPM and it's basics.
